Research Maxim Le Compte

Abstract

Pancreatic ductal adenocarcinoma (PDAC) is a rapidly progressing and usually fatal disease with a 5-year overall survival rate of less than 8%. Despite significant advances in understanding the molecular disease pathways and treatment of cancer, predicting individual responses to both standard of care and targeted therapies remains a stumbling block. The recent introduction of patient-derived tumor organoids as more physiological relevant models has revolutionized both basic and translational cancer research. However, current readouts to study these multicellular constructs only provide limited information. Considering the limitations described above, I aim to develop an innovative and more physiological relevant predictive co-culture platform that implements the effects of cancer associated fibroblast (CAFs) and hypoxia on treatment response. By using these state-of-the-art high-throughput multiplex endpoint and real-time live-cell imaging assays, I will screen a broad range of rationally designed combination strategies. Through this approach, I aim to unravel more effective and personalized combination strategies for pancreatic cancer. Eventually, I will also associate treatment sensitivity of the most promising combinations with gene mutation and expression signatures to identify novel predictive biomarkers for our innovative combination strategies.

Researcher(s)

• Promoter: Peeters Marc
• Co-promoter: Deben Christophe
• Fellow: Le Compte Maxim

Research team(s)

• Center for Oncological Research (CORE)

Project type(s)

• Research Project

Abstract

Pancreatic ductal adenocarcinoma (PDAC) is a rapidly progressing and usually fatal disease with a 5-year overall survival rate of less than 8%. Despite significant advances in understanding the molecular disease pathways and treatment of cancer, predicting individual responses to both standard of care and targeted therapies remains a stumbling block. This limited response rate is a result of the heterogeneity combined with an inadequate understanding of the complexity of the tumor microenvironment of PDAC. Therefore, tremendous efforts have been made in developing more physiologically relevant in vitro models that can accurately predict clinical outcome. Even though two-dimensional (2D) in vitro cancer cell lines have been widely used to unravel the molecular mechanism of tumor growth, these models are not able to mimic the in vivo complexity of PDAC. Patient-derived organoids on the other hand represent a more physiologically relevant model because they preserve the cellular heterogeneity and morphology of the primary tumor tissue. However, current readouts to study these multicellular constructs only provide limited information. Considering the major hurdles described above, we aim to develop an innovative and more physiological relevant predictive platform that implements the effects of cancer associated fibroblast (CAFs) and hypoxia on treatment response. By using these state-of-the-art high-throughput multiplex endpoint and real-time live-cell imaging assays we will screen a broad range of rationally designed combination strategies. Through this approach, we aim to unravel more effective and personalized combination strategies for pancreatic cancer. Eventually, we will also associate treatment sensitivity of the most promising combinations with gene mutation and expression signatures to identify novel predictive biomarkers for our innovative combination strategies.

Researcher(s)

• Promoter: Peeters Marc
• Co-promoter: Deben Christophe
• Fellow: Le Compte Maxim

Research team(s)

• Center for Oncological Research (CORE)

Project type(s)

• Research Project